ExampleSegRuntimeBatchInspection.cpp

This is an example of how to inspect sample images by using Batch inspection mode of Segmentation module. More details about this example.

 
#include "SegmentationTestDLL.h"
#include "SRUtils.h"
 
/* define this variable when user want to use new score threshold other than preset values. */
//#define SR_USE_NEW_SCORE_THRESHOLD 
 
/* define this variable when user want to use new pixel size threshold other than preset values. */
//#define SR_USE_NEW_PIXEL_SIZE_THRESHOLD 
 
/* define this variable when user want filter contours smaller than a threshold of pixel size by calculating appoximate contour sizes.*/
#define SR_FILTER_OBJECTS_BY_APPROXIMATE_SIZE 
 
/* define this variable when user want filter contours smaller than a threshold of pixel size by calculating appoximate contour sizes.*/
//#define SR_FILTER_OBJECTS_BY_EXACT_SIZE 
 
/* define this variable when user want to get cropped image of segmented objects.*/
//#define SR_CROP_AND_STORE_SEGMENTED_OBJECT 
 
/* define this variable when user want to get scores(average, min, max) of semgmented objects.*/
//#define SR_GET_SCORES_OF_SEGMENTED_OBJECT
 
/* define this variable when user want to get average color(in BGR order) of semgmented objects.*/
//#define SR_GET_AVERAGE_COLOR_OF_SEGMENTED_OBJECT
 
/* define this variable when user want to limit the forward time of classfication algorithm. */
//#define SR_USE_FORWARD_TIME_LIMIT
 
int main()
{
    /***********************************************************
    *    최대 이미지 수 설정 , Set max # of images to inspect at once
    *     - 한번에 검사할 최대 이미지 수를 설정합니다. (Set maximum number of images to inspect at once)
    *     - 기본적으로 2의 배수로 설정할 것을 추천드리며 크게 설정할 수록
    *       GPU 메모리 할당량이 커지니 메모리가 부족하다면 더 작은 수로 설정해주세요.
    *       (Recommend to set this value as multiple of 2, and if GPU memory is low, set the value as lower number.)
    *     - 최대 이미지 수 보다 많은 이미지가 들어올 시에 내부적으로 여러번의 검사를 수행하게 됩니다.
    *       (If more images than nMaxImages are passed to the inspection function, multiple inspections are conducted internally.)
    ***********************************************************/
    int          nMaxImages = 8; /* 2, 4, 8, 16, 32, 64 ... */
 
    std::wstring data_dir = L"../../../examples/data";
 
    // trained model path
    std::wstring trainedModelPath = data_dir + L"/models/pcb_sample_model.srSeg";
 
    // image paths to classify
    std::vector<std::wstring> imagePaths(4);
    imagePaths[0] = data_dir + L"/images/PCB/Image-1.png";
    imagePaths[1] = data_dir + L"/images/PCB/Image-2.png";
    imagePaths[2] = data_dir + L"/images/PCB/Image-3.png";
    imagePaths[3] = data_dir + L"/images/PCB/Image-4.png";
 
    eErrorType srStatus = eErrorType::NO_ERROR_OUT;
 
    /***********************************************************
    *    0. 이미지 인풋 타입 설정
    *    inputImageType -
    *             UCGrayImage:  from memory(8bit unsigned char gray image)
    *             UCColorImage: from memory(8bit unsigned char color image)
    *             UIColorImage: from memory(32bit unsigned int color image)
    ***********************************************************/
    //eSRInputImageType inputImageType = eSRInputImageType::UCGrayImage;
    eSRInputImageType inputImageType = eSRInputImageType::UCColorImage;
    //eSRInputImageType inputImageType = eSRInputImageType::UIColorImage;
 
    /***********************************************************
    *    0-1. 정보 띄우기 단계 설정 (Verbosity)
    *    verbosity -
    *             0:    only warning
    *             1:    warning + model info
    *             2:    warning + model info + result info
    *             3:    warning + model info + result info + image display
    *    overlayAlpha - display 이미지 = 원본 이미지 * overlayAlpha + segmentation 라벨 * (1 - overlayAlpha)
    *    displayTime - 결과 이미지 display 하는 시간 (milliseconds)
    *    resizeFactor - 결과 이미지 display 하는 스케일 (display 크기 = 원본 크기 * resizeFactor)
    ***********************************************************/
    int verbosity = 3;
    double overlayAlpha = 0.7;
    int displayTime = 100;
    double resizeFactor = 0.5;
 
    /***********************************************************
    *    0-2. GPU 번호 설정
    *    gpuIndex - 사용하고자 하는 gpu의 번호
    *       Multi GPU 가 가능한 License의 경우에 한하여 gpuIndex를
    *       0~3까지 설정해 줄 수 있다. 아닌 경우 gpuIndex는 0을 사용하여야 한다.
    ***********************************************************/
    int gpuIndex = 0;
 
    /***********************************************************
    *    0-3. Segmentation 검사 방법 설정
    ***********************************************************/
    int outputMode = SR_MULTI_CLASS_CV_CONTOUR_OUTPUT;
 
    /***********************************************************
    *    1.  모델 파일 및 검사 결과 handle 생성,
    *        Create handles for model files and inspection results
    ***********************************************************/
    SegTestHandle handle = SR_CreateSegTestHandleByWideCharPath(trainedModelPath.c_str());
    if (handle == nullptr)
    {
        std::wcout << "Saige Vision failed: Please check the file path of trained model." << std::endl;
    }
 
    int nTotalImages = imagePaths.size();
    std::vector<SegTestResult> segTestResults(nTotalImages);
 
    for (size_t i_img = 0; i_img < nTotalImages; i_img++)
    {
        segTestResults[i_img] = SR_MakeSegTestResult();
    }
 
    /***********************************************************
    *    2. 클래스 정보 확인 (Optional)
    *       Check class information
    ***********************************************************/
    // Print segmentation Test Parameters() by calling one fuctions
    srStatus = SR_PrintSegTestParametersWithoutBG(handle);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that the handle is not created.
        SR_DestroySegTestHandle(handle);
        return -1;
    }
 
    int numberOfClassesOutput = 0;
    int maxStrLenOfClassName = 0;
 
    // Get the number of classes
    srStatus = SR_GetSegTestNumberOfClassesWithoutBG(handle, &numberOfClassesOutput);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that input pointer is not allocated (nullptr).
        SR_DestroySegTestHandle(handle);
        return -1;
    }
 
    // Get the maximum length of class names
    srStatus = SR_GetSegTestMaxStrLenOfClassName(handle, &maxStrLenOfClassName);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that input pointer is not allocated (nullptr).
        SR_DestroySegTestHandle(handle);
        return -1;
    }
 
    std::vector<std::string> classNames(numberOfClassesOutput);
    std::vector<std::wstring> wClassNames(numberOfClassesOutput);
    std::vector<int> classColorsOutput(numberOfClassesOutput);
    
    char* classNameOutput = new char[maxStrLenOfClassName];
    wchar_t* wClassNameOutput = new wchar_t[maxStrLenOfClassName];
 
    for (int i = 0; i < numberOfClassesOutput; i++)
    {
        // Get a color of single class
        srStatus = SR_GetSegTestOneClassColorByIdxWithoutBG(handle, i, &(classColorsOutput[i]));
        if (srStatus != NO_ERROR_OUT)
        {
            PrintErrorType(srStatus, verbosity);
            // if you get this error, it's possible that the input index is out of bound(# of classes) or input pointer is not allocated (nullptr).
            SR_DestroySegTestHandle(handle);
            return -1;
        }
 
        // Get a name of single class by multi byte char (char*)
        srStatus = SR_GetSegTestOneClassNameWithMultiByteCharByIdxWithoutBG(handle, i, classNameOutput);
        if (srStatus != NO_ERROR_OUT)
        {
            PrintErrorType(srStatus, verbosity);
            // if you get this error, it's possible that the input index is out of bound(# of classes) or input pointer is not allocated (nullptr).
            SR_DestroySegTestHandle(handle);
            return -1;
        }
 
        // Get a name of single class by wide char (wchar_t*)
        srStatus = SR_GetSegTestOneClassNameWithWideCharByIdxWithoutBG(handle, i, wClassNameOutput);
        if (srStatus != NO_ERROR_OUT)
        {
            PrintErrorType(srStatus, verbosity);
            // if you get this error, it's possible that the input index is out of bound(# of classes) or input pointer is not allocated (nullptr).
            SR_DestroySegTestHandle(handle);
            return -1;
        }
        classNames[i] = classNameOutput;
        wClassNames[i] = wClassNameOutput;
    }
 
    delete[] classNameOutput;
    delete[] wClassNameOutput;
 
    /***********************************************************
    *    3. 검사 옵션 확인 및 수정 (Optional)
    *       Check inspection options and set new values
    ***********************************************************/
    std::vector<int> scoreThresholdOutput(numberOfClassesOutput);
    std::vector<int> pixelSizeThresholdOutput(numberOfClassesOutput);
    // Get the score threshold for each class
    srStatus = SR_GetSegTestMinScoreThresholdWithoutBG(handle, scoreThresholdOutput.data());
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that input pointer is not allocated (nullptr).
        SR_DestroySegTestHandle(handle);
        return -1;
    }
 
    // Get the area threshold for each class
    srStatus = SR_GetSegTestMinPixelSizeThresholdWithoutBG(handle, pixelSizeThresholdOutput.data());
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that input pointer is not allocated (nullptr).
        SR_DestroySegTestHandle(handle);
        return -1;
    }
 
# ifdef SR_USE_NEW_SCORE_THRESHOLD
    std::vector<int> scoreThreshold(numberOfClassesOutput);
 
    for (int i = 0; i < numberOfClassesOutput; i++)
    {
        scoreThreshold[i] = 100; // Set the new score threshold for each class
    }
    srStatus = SR_SetSegTestMinScoreThresholdWithoutBG(handle, numberOfClassesOutput, scoreThreshold.data(), true);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that the input pointer is not allocated (nullptr).
        SR_DestroySegTestHandle(handle);
        return -1;
    }
#endif
 
# ifdef SR_USE_NEW_PIXEL_SIZE_THRESHOLD
    std::vector<int> pixelSizeThreshold(numberOfClassesOutput);
 
    for (int i = 0; i < numberOfClassesOutput; i++)
    {
        pixelSizeThreshold[i] = 0; // Set the new pixel-size threshold for each class
    }
    srStatus = SR_SetSegTestMinPixelSizeThresholdWithoutBG(handle, numberOfClassesOutput, pixelSizeThreshold.data(), true);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that the input pointer is not allocated (nullptr).
        SR_DestroySegTestHandle(handle);
        return -1;
    }
    pixelSizeThresholdOutput = pixelSizeThreshold;
#endif
 
#if (!defined(SR_FILTER_OBJECTS_BY_EXACT_SIZE)) && (!defined(SR_FILTER_OBJECTS_BY_APPROXIMATE_SIZE))
    srStatus = SR_SetSegTestModeOfFilteringByPixelSize(handle, eModeOfPixelSizeFilter::SR_PIXELSIZE_FILTER_MODE_OFF, true);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that handle is not created or filter mode is not appropriate.
        SR_DestroySegTestHandle(handle);
        return -1;
    }
#endif
 
#ifdef SR_FILTER_OBJECTS_BY_EXACT_SIZE
    // "SaigeVision Error: CANNOT USE FILTERING FUNCTION IN CURRENT OUTPUT MODE"
    assert(outputMode == SR_ONE_CLASS_CV_CONTOUR_OUTPUT || outputMode == SR_MULTI_CLASS_CV_CONTOUR_OUTPUT);
    srStatus = SR_SetSegTestModeOfFilteringByPixelSize(handle, eModeOfPixelSizeFilter::SR_PIXELSIZE_FILTER_MODE_ON_EXACT, true);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that handle is not created or filter mode is not appropriate.
        SR_DestroySegTestHandle(handle);
        return -1;
    }
#endif
 
#ifdef SR_FILTER_OBJECTS_BY_APPROXIMATE_SIZE
    // "SaigeVision Error: CANNOT USE FILTERING FUNCTION IN CURRENT OUTPUT MODE"
    assert(outputMode == SR_ONE_CLASS_CV_CONTOUR_OUTPUT || outputMode == SR_MULTI_CLASS_CV_CONTOUR_OUTPUT);
    srStatus = SR_SetSegTestModeOfFilteringByPixelSize(handle, eModeOfPixelSizeFilter::SR_PIXELSIZE_FILTER_MODE_ON_FAST, true);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that handle is not created or filter mode is not appropriate.
        SR_DestroySegTestHandle(handle);
        return -1;
    }
#endif
 
#ifdef SR_CROP_AND_STORE_SEGMENTED_OBJECT
    int cropSize = 256;
    srStatus = SR_SetSegTestIsToStoreCroppedImage(handle, true, cropSize, eModeOfCropLargeObjects::SR_CROP_BY_OBJECTSIZE_N_RESIZE_TO_CROPSIZE, true);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that handle is not created.
        SR_DestroySegTestHandle(handle);
        return -1;
    }
#endif
 
#ifdef SR_GET_SCORES_OF_SEGMENTED_OBJECT
    srStatus = SR_SetSegTestIsToCalcObjectScores(handle, true, true);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that handle is not created.
        SR_DestroySegTestHandle(handle);
        return -1;
    }
#endif
 
#ifdef SR_GET_AVERAGE_COLOR_OF_SEGMENTED_OBJECT
    srStatus = SR_SetSegTestIsToCalcAverageColor(handle, true, true);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that handle is not created.
        SR_DestroySegTestHandle(handle);
        return -1;
    }
#endif
 
#ifdef SR_USE_FORWARD_TIME_LIMIT
    float timeLimit = 1000; // milliseconds
    srStatus = SR_SetSegTestForwardTimeLimitVariables(handle, timeLimit, true, true);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that handle is not created.
        SR_DestroySegTestHandle(handle);
        return -1;
    }
#endif
#ifndef SR_USE_FORWARD_TIME_LIMIT
    srStatus = SR_SetSegTestIsToUseForwardTimeLimit(handle, false, true);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that handle is not created.
        SR_DestroySegTestHandle(handle);
        return -1;
    }
#endif
 
    /***********************************************************
    *    4. 메모리를 GPU 및 CPU에 할당합니다.
    *       Allocate GPU and CPU memory
    ***********************************************************/
    srStatus = SR_LoadSegTestNetworkInGPU(handle, gpuIndex, nMaxImages);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that the inserted index is not applicable.
        SR_DestroySegTestHandle(handle);
        return -1;
    }
 
 
    std::vector<int>           heights(nTotalImages, 0);
    std::vector<int>           widths(nTotalImages, 0);
    unsigned char** ucImageData = new unsigned char*[nTotalImages]; // arrays of unsigned char image data
    unsigned int**  uiImageData = new unsigned int*[nTotalImages]; // arrays of unsigned int image data
 
    for (size_t i_img = 0; i_img < nTotalImages; i_img++)
    {
        uiImageData[i_img] = nullptr;
        ucImageData[i_img] = nullptr;
 
        if (inputImageType == eSRInputImageType::UIColorImage)
        {
            uiImageData[i_img] = (unsigned int*)GetImageDataByInputImageType(imagePaths[i_img], inputImageType, heights[i_img], widths[i_img]);
        }
        else
        {
            ucImageData[i_img] = (unsigned char*)GetImageDataByInputImageType(imagePaths[i_img], inputImageType, heights[i_img], widths[i_img]);
        }
    }
 
    /***********************************************************
    *    5. 이미지 검사하기, Inspect images
    ***********************************************************/
    eErrorType errorType = NO_ERROR_OUT;
    if (inputImageType == eSRInputImageType::UCGrayImage)
    {
        errorType = SR_ApplySegTestFromMultiple8bitCharGrayArrays(handle, nTotalImages, ucImageData, heights.data(), widths.data(), segTestResults.data(), outputMode);
    }
    else if (inputImageType == eSRInputImageType::UCColorImage)
    {
        errorType = SR_ApplySegTestFromMultiple8bitCharColorArrays(handle, nTotalImages, ucImageData, heights.data(), widths.data(), segTestResults.data(), outputMode);
    }
    else if (inputImageType == eSRInputImageType::UIColorImage)
    {
        errorType = SR_ApplySegTestFromMultipleUnsignedIntColorArrays(handle, nTotalImages, uiImageData, heights.data(), widths.data(), segTestResults.data(), outputMode);
    }
 
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        std::cout << "Fail to segment" << std::endl;
    }
    else
    {
        /***********************************************************
        *    6. 검사 결과 받아오기, Retrieve inspection results
        ***********************************************************/
        if (verbosity >= 2)
        {
            long inspectionTimeOutput = 0, loadImageTimeOutput = 0;
 
            // Get the inspection time
            srStatus = SR_GetSegTestInspectionTime(segTestResults[0], &inspectionTimeOutput);
            if (srStatus != NO_ERROR_OUT)
            {
                // if you get this error, it's possible that the input pointer is not allocated (nullptr).
                PrintErrorType(srStatus, verbosity);
            }
 
            // Get the image-loading time
            srStatus = SR_GetSegTestLoadImageTime(segTestResults[0], &loadImageTimeOutput);
            if (srStatus != NO_ERROR_OUT)
            {
                // if you get this error, it's possible that the input pointer is not allocated (nullptr).
                PrintErrorType(srStatus, verbosity);
            }
            std::cout << "====== Segmentation Test Result (Total) ======" << std::endl;
            std::cout << "  - Total Image Loading time: " << loadImageTimeOutput << std::endl;
            std::cout << "  - Total Inspection time: " << inspectionTimeOutput << std::endl;
        }
        for (size_t i_img = 0; i_img < imagePaths.size(); i_img++)
        {
            /* Getting output matrix is optional when output mode is SR_ONE_CLASS_CV_CONTOUR_OUTPUT or SR_MULTI_CLASS_CV_CONTOUR_OUTPUT.
            * Output matrix 를 가져오는 과정은 SR_ONE_CLASS_CV_CONTOUR_OUTPUT or SR_MULTI_CLASS_CV_CONTOUR_OUTPUT 두 모드일 경우 optional 입니다.
            */
            int matrixChannelOutput = 0, matrixSizeOutput = 0;
            std::vector<unsigned char> outputMatrix;
            if (outputMode == SR_ONE_CLASS_MATRIX_OUTPUT ||
                outputMode == SR_MULTI_CLASS_MATRIX_OUTPUT ||
                outputMode == SR_MULTI_CLASS_HEATMAP_OUTPUT)
            {
                // Get the dimension of output matrix
                srStatus = SR_GetSegTestOutputMatrixSizeInfo(segTestResults[i_img], &matrixChannelOutput, &matrixSizeOutput);
                if (srStatus != NO_ERROR_OUT)
                {
                    PrintErrorType(srStatus, verbosity);
                    // if you get this error, it's possible that the input pointer is not allocated (nullptr).
                    continue; // jump to next image
                }
 
                outputMatrix.resize(matrixSizeOutput);
                // Get the data of output matrix (score matrix)
                srStatus = SR_GetSegTestOutputMatrix(segTestResults[i_img], matrixSizeOutput, outputMatrix.data());
                if (srStatus != NO_ERROR_OUT)
                {
                    PrintErrorType(srStatus, verbosity);
                    // if you get this error, it's possible that the input's size(matrixSizeOutput) does not match the result's or input pointer is not allocated (nullptr).
                    continue; // jump to next image
                }
            }
 
            // Display and draw
            if (verbosity >= 2)
            {
                std::cout << "====== Segmentation Test Result ======" << std::endl;
                std::wcout << imagePaths[i_img] << std::endl;
                std::cout << "  - Image width, height: " << widths[i_img] << ", " << heights[i_img] << std::endl;
            }
 
            // Get Segmented objects
            // segmented object after filtering (# of classes x # of objects)
            std::vector<SR_SegmentedObject> segmentedObject;
 
            // deleted object after filtering (# of classes x # of objects)
            std::vector<SR_SegmentedObject> deletedObject;
 
            if (outputMode == SR_ONE_CLASS_CV_CONTOUR_OUTPUT || outputMode == SR_MULTI_CLASS_CV_CONTOUR_OUTPUT)
            {
                int nSegmentedObjects = 0;
                srStatus = SR_GetSegTestNumberOfSegmentedObjects(segTestResults[i_img], &nSegmentedObjects);
                if (srStatus != NO_ERROR_OUT)
                {
                    PrintErrorType(srStatus, verbosity);
                    // if you get this error, it's possible that the segTestResult is not created (nullptr).
                    continue; // jump to next image
                }
                segmentedObject.resize(nSegmentedObjects);
 
                srStatus = SR_GetSegTestSegmentedObjects(segTestResults[i_img], segmentedObject.data());
                if (srStatus != NO_ERROR_OUT)
                {
                    PrintErrorType(srStatus, verbosity);
                    // if you get this error, it's possible that the segTestResult is not created (nullptr).
                    continue; // jump to next image
                }
 
                if (verbosity >= 2)
                {
                    std::cout << "  - the number of segmented objects: " << segmentedObject.size() << std::endl;
                    for (int i = 0; i < segmentedObject.size(); i++)
                    {
                        std::cout << "  - " << i << "-th segmented objects : " <<
                            "class[" << segmentedObject[i].classIdx << "], " <<
                            "area[" << segmentedObject[i].area << "], " <<
                            "width[" << segmentedObject[i].width << "], " <<
                            "height[" << segmentedObject[i].height << "], " <<
                            "x_center[" << segmentedObject[i].xCenter << "], " <<
                            "y_center[" << segmentedObject[i].yCenter << "], " <<
                            "scores(average, min, max)[" << segmentedObject[i].scores[0] << ", "
                            << segmentedObject[i].scores[1] << ", "
                            << segmentedObject[i].scores[2] << "], "
                            "average_color(B, G, R)[" << segmentedObject[i].averageColor[0] << ", " <<
                            segmentedObject[i].averageColor[1] << ", " <<
                            segmentedObject[i].averageColor[2] << "]" << std::endl;
                    }
                }
 
                // if user want to check the deleted objects by filtering, call this functions.
                int nDeletedObjects = 0;
                srStatus = SR_GetSegTestNumberOfDeletedObjects(segTestResults[i_img], &nDeletedObjects);
                if (srStatus != NO_ERROR_OUT)
                {
                    PrintErrorType(srStatus, verbosity);
                    // if you get this error, it's possible that the segTestResult is not created (nullptr).
                    continue; // jump to next image
                }
                deletedObject.resize(nDeletedObjects);
 
                srStatus = SR_GetSegTestDeletedObjects(segTestResults[i_img], deletedObject.data());
                if (srStatus != NO_ERROR_OUT)
                {
                    PrintErrorType(srStatus, verbosity);
                    // if you get this error, it's possible that the segTestResult is not created (nullptr).
                    continue; // jump to next image
                }
 
                if (verbosity >= 2)
                {
                    std::cout << "  - the number of deleted objects: " << deletedObject.size() << std::endl;
                }
            }
 
            if (verbosity >= 3)
            {
                cv::Mat image = cv::Mat();
 
                if (inputImageType == eSRInputImageType::UCGrayImage)
                {
                    image = cv::Mat(heights[i_img], widths[i_img], CV_8UC1, ucImageData[i_img]);
                    image = image.clone(); /* to prevent modifying raw data */
                }
                else if (inputImageType == eSRInputImageType::UCColorImage)
                {
                    image = cv::Mat(heights[i_img], widths[i_img], CV_8UC3, ucImageData[i_img]);
                    image = image.clone(); /* to prevent modifying raw data */
                }
                else if (inputImageType == eSRInputImageType::UIColorImage)
                {
                    image = cv::Mat(heights[i_img], widths[i_img], CV_8UC4, uiImageData[i_img]);
                    image = image.clone(); /* to prevent modifying raw data */
                }
                else
                {
                    int isSuccess = OpenImageFile(imagePaths[i_img].c_str(), image);
                    if (isSuccess == 0)
                    {
                        std::cout << "Saige Vision FAILED: Fail to open image file to display" << std::endl;
                        continue;
                    }
                }
 
                if (outputMode == SR_ONE_CLASS_MATRIX_OUTPUT || outputMode == SR_ONE_CLASS_CV_CONTOUR_OUTPUT)
                {
                    if (image.channels() == 4)
                    {
                        cv::cvtColor(image, image, cv::COLOR_BGRA2BGR);
                    }
                    cv::Mat imageCheck = image.clone();
                    unsigned char* data = imageCheck.data;
 
                    if (outputMode == SR_ONE_CLASS_MATRIX_OUTPUT)
                    {
                        if (image.channels() == 1)
                        {
                            for (int i_pixel = 0; i_pixel < matrixSizeOutput; i_pixel++)
                            {
                                if (outputMatrix[i_pixel] > 0)
                                {
                                    data[i_pixel] = outputMatrix[i_pixel];
                                }
                            }
                        }
                        else if (image.channels() == 3)
                        {
                            for (int i_pixel = 0; i_pixel < matrixSizeOutput; i_pixel++)
                            {
                                if (outputMatrix[i_pixel] > 0)
                                {
                                    data[3 * i_pixel + 0] = 0;
                                    data[3 * i_pixel + 1] = 0;
                                    data[3 * i_pixel + 2] = outputMatrix[i_pixel];
                                }
                            }
                        }
                    }
                    else if (outputMode == SR_ONE_CLASS_CV_CONTOUR_OUTPUT)
                    {
                        cv::Scalar whiteScalar = cv::Scalar(255.0);
 
                        if (image.channels() == 1)
                        {
                            whiteScalar = cv::Scalar(255.0);
                        }
                        else if (image.channels() == 3)
                        {
                            whiteScalar = cv::Scalar(0, 0, 255.0);
                        }
 
                        for (int i_c = 0; i_c < segmentedObject.size(); i_c++)
                        {
                            std::vector<std::vector<cv::Point>> contours;
                            std::vector<cv::Vec4i> hierarchy;
 
                            convertSRSegmentedObjectToOpencvFormat(segmentedObject[i_c], contours, hierarchy);
 
                            cv::drawContours(imageCheck, contours, -1, whiteScalar, -1, 8, hierarchy);
                        }
                    }
 
                    cv::Mat finalUnifiedImage;
                    double beta = 1.0 - overlayAlpha;
                    double gamma = 0;
                    cv::addWeighted(image, overlayAlpha, imageCheck, beta, gamma, finalUnifiedImage);
 
                    cv::namedWindow("Segmentation Result", cv::WINDOW_NORMAL);
                    cv::resizeWindow("Segmentation Result", widths[i_img] * resizeFactor, heights[i_img] * resizeFactor);
                    cv::imshow("Segmentation Result", finalUnifiedImage);
                    cv::waitKey(displayTime);
                }
                else if (outputMode == SR_MULTI_CLASS_MATRIX_OUTPUT || outputMode == SR_MULTI_CLASS_HEATMAP_OUTPUT || outputMode == SR_MULTI_CLASS_CV_CONTOUR_OUTPUT)
                {
                    if (image.channels() == 4)
                    {
                        cv::cvtColor(image, image, cv::COLOR_BGRA2BGR);
                    }
                    else if (image.channels() == 1)
                    {
                        cv::cvtColor(image, image, cv::COLOR_GRAY2BGR);
                    }
 
                    cv::Mat imageCheck = image.clone();
 
                    if (outputMode == SR_MULTI_CLASS_MATRIX_OUTPUT || outputMode == SR_MULTI_CLASS_HEATMAP_OUTPUT)
                    {
                        unsigned char* data = imageCheck.data;
 
                        for (int i_pixel = 0; i_pixel < widths[i_img] * heights[i_img]; i_pixel++)
                        {
                            int maxVal = 0;
                            int channel = 0;
                            for (int c = 0; c < numberOfClassesOutput; c++)
                            {
                                if (outputMatrix[i_pixel + c * widths[i_img] * heights[i_img]] > maxVal)
                                {
                                    maxVal = outputMatrix[i_pixel + c * widths[i_img] * heights[i_img]];
                                    channel = c;
                                }
                            }
 
                            unsigned char* tmp = (unsigned char*)(&classColorsOutput[channel]);
                            int b = 0, g = 0, r = 0;
                            std::memcpy(&b, tmp, 1);
                            std::memcpy(&g, tmp + 1, 1);
                            std::memcpy(&r, tmp + 2, 1);
 
                            if (maxVal != 0) // else: background
                            {
                                data[3 * i_pixel + 0] = b * (maxVal / 255.0);
                                data[3 * i_pixel + 1] = g * (maxVal / 255.0);
                                data[3 * i_pixel + 2] = r * (maxVal / 255.0);
                            }
                        }
                    }
                    else if (outputMode == SR_MULTI_CLASS_CV_CONTOUR_OUTPUT)
                    {
                        for (int i_c = 0; i_c < segmentedObject.size(); i_c++)
                        {
                            unsigned char* tmp = (unsigned char*)(&classColorsOutput[segmentedObject[i_c].classIdx]);
                            int b = 0, g = 0, r = 0;
                            std::memcpy(&b, tmp, 1);
                            std::memcpy(&g, tmp + 1, 1);
                            std::memcpy(&r, tmp + 2, 1);
 
                            cv::Scalar overlayScalar = cv::Scalar(b, g, r);
 
                            std::vector<std::vector<cv::Point>> contours;
                            std::vector<cv::Vec4i> hierarchy;
 
                            convertSRSegmentedObjectToOpencvFormat(segmentedObject[i_c], contours, hierarchy);
 
                            cv::drawContours(imageCheck, contours, -1, overlayScalar, -1, 8, hierarchy);
                        }
                    }
 
                    cv::Mat finalUnifiedImage;
                    double beta = 1.0 - overlayAlpha;
                    double gamma = 0.0;
                    cv::addWeighted(image, overlayAlpha, imageCheck, beta, gamma, finalUnifiedImage);
 
                    cv::namedWindow("Segmentation Result", cv::WINDOW_NORMAL);
                    cv::resizeWindow("Segmentation Result", widths[i_img] * resizeFactor, heights[i_img] * resizeFactor);
                    cv::imshow("Segmentation Result", finalUnifiedImage);
                    cv::waitKey(displayTime);
 
#ifdef SR_CROP_AND_STORE_SEGMENTED_OBJECT
                    for (int i_c = 0; i_c < segmentedObject.size(); i_c++)
                    {
                        if (segmentedObject[i_c].croppedImage != nullptr)
                        {
                            int dataType = CV_MAKETYPE(CV_8U, segmentedObject[i_c].croppedImageChannels);
                            cv::Mat croppedImage = cv::Mat(segmentedObject[i_c].croppedImageHeight, segmentedObject[i_c].croppedImageWidth, dataType, segmentedObject[i_c].croppedImage);
 
                            cv::namedWindow("Segmentation Cropped Image", cv::WINDOW_NORMAL);
                            cv::resizeWindow("Segmentation Cropped Image", croppedImage.size().width, croppedImage.size().height);
                            cv::imshow("Segmentation Cropped Image", croppedImage);
                            cv::waitKey(displayTime);
                        }
                    }
#endif
                }
            }
        }
 
    }
 
    /***********************************************************
    *    7. Handle 파괴, Destruct handles
    ***********************************************************/
    for (size_t i_img = 0; i_img < nTotalImages; i_img++)
    {
        srStatus = SR_DeleteSegTestResult(segTestResults[i_img]);
        if (srStatus != NO_ERROR_OUT)
        {
            PrintErrorType(srStatus, verbosity);
            // if you get this error, it's possible that the input pointer is not allocated (nullptr).
            return -1;
        }
 
        if (ucImageData[i_img] != nullptr)
        {
            delete[] ucImageData[i_img];
            ucImageData[i_img] = nullptr;
        }
        if (uiImageData[i_img] != nullptr)
        {
            delete[] uiImageData[i_img];
            uiImageData[i_img] = nullptr;
        }
    }
    delete[] uiImageData;
    delete[] ucImageData;
 
    srStatus = SR_DestroySegTestHandle(handle);
    if (srStatus != NO_ERROR_OUT)
    {
        PrintErrorType(srStatus, verbosity);
        // if you get this error, it's possible that the input pointer is not allocated (nullptr).
        return -1;
    }
 
    std::cout << "SaigeVision successfully completed, please press enter key to terminate" << std::endl;
    std::cin.get();
    return 0;
}